PURPOSE: The aims of this study were to identify the most significant prognostic factors in myelodysplastic syndromes (MDS) taking into account both their values at clinical onset and their changes in time and to develop a dynamic model for predicting survival and leukemic evolution that can be applied at any time during the course of the disease. PATIENTS AND METHODS: We studied a learning cohort of 426 MDS patients diagnosed at the Department of Hematology, San Matteo Hospital, Pavia, Italy, between 1992 and 2004, and a validation cohort of 739 patients diagnosed at the Heinrich-Heine-University Hospital, Düsseldorf, Germany, between 1982 and 2003. All patients were reclassified according to WHO criteria. Univariable and multivariable analyses were performed using Cox models with time-dependent covariates. RESULTS: The most important variables for the prognostic model were WHO subgroups, karyotype, and transfusion requirement. We defined a WHO classification-based prognostic scoring system (WPSS) that was able to classify patients into five risk groups showing different survivals (median survival from 12 to 103 months) and probabilities of leukemic evolution (P < .001). WPSS was shown to predict survival and leukemia progression at any time during follow-up (P < .001), and its prognostic value was confirmed in the validation cohort. CONCLUSION: WPSS is a dynamic prognostic scoring system that provides an accurate prediction of survival and risk of leukemic evolution in MDS patients at any time during the course of their disease. This time-dependent system seems particularly useful in lower risk patients and may be used for implementing risk-adapted treatment strategies.
PURPOSE: The aims of this study were to identify the most significant prognostic factors in myelodysplastic syndromes (MDS) taking into account both their values at clinical onset and their changes in time and to develop a dynamic model for predicting survival and leukemic evolution that can be applied at any time during the course of the disease. PATIENTS AND METHODS: We studied a learning cohort of 426 MDSpatients diagnosed at the Department of Hematology, San Matteo Hospital, Pavia, Italy, between 1992 and 2004, and a validation cohort of 739 patients diagnosed at the Heinrich-Heine-University Hospital, Düsseldorf, Germany, between 1982 and 2003. All patients were reclassified according to WHO criteria. Univariable and multivariable analyses were performed using Cox models with time-dependent covariates. RESULTS: The most important variables for the prognostic model were WHO subgroups, karyotype, and transfusion requirement. We defined a WHO classification-based prognostic scoring system (WPSS) that was able to classify patients into five risk groups showing different survivals (median survival from 12 to 103 months) and probabilities of leukemic evolution (P < .001). WPSS was shown to predict survival and leukemia progression at any time during follow-up (P < .001), and its prognostic value was confirmed in the validation cohort. CONCLUSION: WPSS is a dynamic prognostic scoring system that provides an accurate prediction of survival and risk of leukemic evolution in MDSpatients at any time during the course of their disease. This time-dependent system seems particularly useful in lower risk patients and may be used for implementing risk-adapted treatment strategies.
Authors: John F Seymour; Pierre Fenaux; Lewis R Silverman; Ghulam J Mufti; Eva Hellström-Lindberg; Valeria Santini; Alan F List; Steven D Gore; Jay Backstrom; David McKenzie; C L Beach Journal: Crit Rev Oncol Hematol Date: 2010-05-06 Impact factor: 6.312
Authors: Maximilian Stahl; Michelle DeVeaux; Theo de Witte; Judith Neukirchen; Mikkael A Sekeres; Andrew M Brunner; Gail J Roboz; David P Steensma; Vijaya R Bhatt; Uwe Platzbecker; Thomas Cluzeau; Pedro H Prata; Raphaël Itzykson; Pierre Fenaux; Amir T Fathi; Alexandra Smith; Ulrich Germing; Ellen K Ritchie; Vivek Verma; Aziz Nazha; Jaroslaw P Maciejewski; Nikolai A Podoltsev; Thomas Prebet; Valeria Santini; Steven D Gore; Rami S Komrokji; Amer M Zeidan Journal: Blood Adv Date: 2018-07-24
Authors: Mikkael A Sekeres; W Marieke Schoonen; Hagop Kantarjian; Alan List; Jon Fryzek; Ronald Paquette; Jaroslaw P Maciejewski Journal: J Natl Cancer Inst Date: 2008-10-28 Impact factor: 13.506